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Background: Advance technology and diagnostic approach to disease has increased workload burden on laboratory and timely reporting as well as reliable results provision to physicians is another challenge of laboratory.Objective: To compare pre-analytical errors, peak volume, flow rate and turn around time before and after total lab automation in clinical laboratory.Study type,settings& duration:A cross-sectional study was carried out at Shifa International Hospital, Islamabad from January 2018 to December 2020.Methodology:Present study assesses the key performance indicators based on the Total Lab Automation (TLA) work flow. Frequency and percentage was used to calculate increase in test volume before and after TLA implementation while one way ANOVA was used to test the difference in Turnaround Time (TAT).Results:Prior TLA average turnaround time(TAT) for routine chemistry was 4 hours while immunoassay TAT average reported was 6-7 hours.Post TLA average TAT for routine chemistry is 1-2 hour while for immunoassay it 3-4 hours. There is 5-10% increase in test volume after the TLA implementation. TAT shows significant mean difference before and after TLA installation (pvalue=0.00).Conclusion:In current study, findings prove that overall efficiency, turnaround time overall workload and optimize workflow occurred after implementation of TLA.Monitoring of key metrics help in continuous process improvement. Advance technology in laboratory makes work easier which was previously laborious.

Subway station-level peak hour ridership (SPR) is a crucial input parameter for multiple applications, including the planning, design, construction, and operation of stations. However, traditional SPR estimation techniques may produce biased results. A unified peak hour factor (PHF) extracted from the line level is generally set for all attributed stations, which ignores the possible peak deviation that arises between the station and line and the wide variation of PHFs in practice. This study presents a comprehensive and refined estimation framework for SPR that accommodates the peak deviation context by introducing the peak deviation coefficient (PDC). Moreover, the estimation of the PDC and PHF variability is improved by constructing spatial regression based relationship models. The empirical results show that the proposed approach exhibits wider applicability and a higher prediction precision across all types of peak periods considered as compared to conventional methods (i.e., MAPE decreases of 0.115–0.351). The findings demonstrate the importance of the consideration of the peak deviation scenario and the spatial dependency in SPR estimation to achieve better decision making. Moreover, the underlying influencing mechanism of the PHF and PDC at distinct peak periods is further revealed using the spatial model. This provides critical theoretical references and policy implications to prudently deploy land-use resources to balance the travel demand between peak and off-peak periods and thus enhance the line operation efficiency.

The park and ride (P&R) parking type is usually located near peripheral public transport stops. These parking places are dedicated to people who, after leaving their vehicle in the parking, continue their journey to the city center using some form of public transport such as bus, metro, rail or tram systems. This article aims to examine the features associated with P&R parking locations in use in Cracow (Poland). The analysis included the number of entries and exits to and from parking during particular periods of the day, week and year, parking time of vehicles, and parking space use. A parking peak hour factor was also calculated, which expresses the crowding degree of vehicle entries/exits in/out parking during a particular period. In addition, the paper presents an analysis of factors determining users to P&R parking use. In the modeling process, logit models were used, which, as stated after analyzing the literature on the subject, were already used in various countries around the world to describe the behavior of P&R parking users. However, so far, such research relating to Polish conditions has been not published in the available literature. The obtained results allowed to state that the most important factors determining the likelihood of using P&R parking in Cracow are age, number of years having a driving license, monthly income (gross), and an average number of trips made during a day. Other variables, which not included in the study, can influence the P&R parking use. However, the presented results are the basis for conducting furtherer, more in-depth analyses based on a larger number of independent variables that may determine the P&R parking use.

Urban traffic congestion poses significant challenges to sustainable development in rapidly growing cities. This study examines the spatiotemporal dynamics of traffic congestion in Enugu, Nigeria, a representative mid-sized sub-Saharan city, through a comprehensive analysis of volumetric traffic flows along three major distributors: Abakpa, Nike, and Trans-Ekulu Road. The research employed direct observation and vehicle counts, conducting a week-long traffic census during peak morning (7:30–9:30 AM) and evening (4:00–8:00 PM) periods. Data was analyzed using peak hour factor (PHF), mean plots, and chi-square tests. The results reveal a daily mean of 2334 vehicles/h. Abakpa/Nike Road demonstrated the highest traffic volumes (mean = 809.2 vehicles/h) and most concentrated peak flows (PHF = 0.79), while Trans-Ekulu Road exhibited lower, more uniformly distributed volumes (mean = 719.4 vehicles/h, PHF = 0.93). Evening peaks (6:00–8:00 PM) consistently surpassed morning volumes, with Abakpa/Nike Road reaching 974 vehicles/hour during the evening rush compared to 620 vehicles/hour in the mornings. Chi-square analysis (χ2 = 55.5, df = 8) confirmed statistically significant differences in flow distribution among the routes. The complete absence of Monday traffic due to regional “sit-at-home” orders created a distinctive weekly pattern, with Tuesdays experiencing disproportionate congestion as the de facto first workday. Non-linear relationships between volume increases and congestion severity were observed, where modest volume changes produced amplified system-wide effects. Spatial analysis revealed that evening congestion disparities between distributors (14.9%) significantly exceeded morning differences (8.9%), indicating uneven network utilization. These findings illuminate how socio-political factors, activity patterns, and complex network dynamics shape urban mobility in rapidly developing contexts. This study offers empirical evidence supporting targeted interventions, including Tuesday-specific traffic management, evening-focused congestion mitigation strategies, and corridor-specific infrastructure improvements to enhance mobility in this representative mid-sized sub-Saharan city.

The peak hour factor is commonly used in the analysis and design of water distribution networks as a function of population. However, water storage tanks in buildings significantly influence diurnal flow variations of these networks, yet their impact has been insufficiently quantified. In this study, a model is developed to generate flow patterns based on the proportion of buildings equipped with water storage tanks in water distribution networks. The model incorporates various parameters, including population size and average pressure. The results show that if all consumers have water storage tanks in their buildings, the maximum water demand coefficient can decrease by up to 28%, while minimum night flow increases by up to 400%. The proposed model provides a valuable tool for the design and operation of water distribution networks in areas where building water storage tanks is prevalent.

Increase of traffic volumes at intersections is one of the significant reasons that cause congestion. The current study aims to analyze, assess and improve traffic performance of (Al-Safeena roundabout) and (Sayed Jawda intersection) in Kerbala city. In order to implement these goals, data of the traffic volume and geometric layout for (Al-Safeena roundabout) and (Sayed Jawda intersection) are manually collected. To achieve the traffic analysis process, (SIDRA version 7) traffic program was used. Al-Safeena intersection improved by adding a new lane for each approach, this reduce the delay times, fuel consumption, and stops. This process will reduce ratio of volume to capacity (v/c) to value 0.7 and the delay rate value to 15.3 sec and LOS to C. On other hand, traffic operation at Sayed Jawda intersection is relatively enhanced by dividing the width of approach to four lanes, however, level of service is not upgraded satisfactorily. Change Sayed Jawda intersection to roundabout reduces the value of v/c to 0.8, and the value of average delay to 24 sec and LOS C.

With a sustained growth of cars, many urban traffic problems caused by cars should be solved as soon as possible. However, basic urban traffic investigation and analysis of current situation is the the basis and the key to solve the problems. In this paper, investigation of traffic volume and pot speed is done at DongYing Road in Hohhot, and basic data base is established. Some significant parameters of urban traffic are calculated, and current traffic situation of the investigated road is detailedly analysed and studied. What is more, some problems found during the investigation are proposed and corresponding feasible suggestions are given.

The peak hour factor characterizes the fluctuations of traffic flow based on the busiest 15 minutes during the peak hour. This parameter is used in the process of evaluating the traffic flow conditions such as capacity and Level of Service. The paper examines the impact of traffic on the PHF, and results in calibrated models that estimate the PHF of different road categories based on a large data set obtained from a recent cordon and screen line traffic survey of Tel Aviv metropolitan area in Israel. The PHF was estimated for four road categories based on the independent variables: total number of vehicles per lane per direction, total number of trucks per direction, and total number of buses per direction. The PHF resulted in the following ranges, based on the superior calibrated models with or without the impact of the heavy vehicle variables: 0.88–0.99 for rural freeways and multilane highways, 0.81–0.97 for two-lane rural highways, 0.922–0.972 for urban and suburban freeways and multilane highways, and 0.91–0.98 for urban arterials. These volume based ranges partially include the uniform values proposed by the Highway Capacity Manual (2000, 2010). The rural model calibrations could be beneficial in estimating traffic flow rates for traffic engineering analysis when traffic counting is incomplete or not achievable. The urban models need further examination.

The effect of aggressive driving behavior on driver’s injury severity is analyzed by considering a comprehensive set of variables at highway-rail grade crossings in the US. In doing so, we are able to use a mixed logit modelling approach; the study explores the determinants of driver-injury severity with and without aggressive driving behaviors at highway-rail grade crossings. Significant differences exist between drivers’ injury severity with and without aggressive driving behaviors at highway-rail grade crossings. The level of injury for younger male drivers increases a lot if they are with aggressive driving behavior. In addition, driving during peak-hour is found to be a statistically significant predictor of high level injury severity with aggressive driving behavior. Moreover, environmental factors are also found to be statistically significant. The increased level of injury severity accidents happened for drivers with aggressive driving behavior in the morning peak (6-9 am), and the probability of fatality increases in both snow and fog condition. Driving in open space area is also found to be a significant factor of high level injury severity with aggressive driving behaviors. Bad weather conditions are found to increase the probability of drivers’ high level injury severity for drivers with aggressive driving behaviors.

While traffic speed is a popular metric for evaluating road network efficiency and safety, its application is often limited. Studies typically focus on short stretches of road, specific design features, or particular highways, and even then, only during certain timeframes. This might be due to a lack of comprehensive traffic data. Further, analysing entire road networks and landuse patterns at a macro-level has been less common. Therefore, the study’s objective is to understand the factors influencing traffic speed variations at a zonal level using crowdsourced pervasive traffic data. Crowdsourced Speed (CS) data was collected every 10 minutes for thirty consecutive days in the Central Business District (CBD) area in Sydney, Australia. Data on road network characteristics, land use, public transportation, socioeconomic variables, and travel behaviour at the zonal level were gathered from various sources for analysis. Principal Component Analysis (PCA) based local regression models were developed to understand the variables influencing speed variation at the zonal level for both morning and evening peak hours. Two Geographically Weighted Regression (GWR) models were developed to examine spatial variability in the coefficient of variation (CoV) of speed. The spatial distribution of local R² indicates that it ranges from 0.102 to 0.57 for the morning peak hour and from 0.37 to 0.54 for the evening peak hour. The variable coefficients indicate that compared to the morning peak-hour model, the evening peak-hour model exhibits greater consistency and statistical significance (at the 0.10 level) across most zones in explaining the coefficient of variation (CoV) of speed. For the evening peak hour, density, private vehicle travel, commercial activity, land use diversity and income, and road network connectivity factors are significant in explaining the variability. Analysing speed variability at the zonal level across a large network will enable planners and engineers to prioritise zones for traffic improvements.